How Facebook Topic Data Helped One Ad Agency Perfect a Campaign

The image of the Don Draper style creative genius who comes up with great ideas on the fly is a tough one for advertising creatives to live up to. However, the mad men of today do have more tools at their disposal than their 60s counterparts. While the customers of Sterling Cooper only find out whether Don’s idea was any good after it has appeared in the New York Times, these days agencies can perform detailed audience research to understand how their target market engage with their advertising concepts.

One ad agency were working with a brand and had such a concept to explore before they asked their client to commit to a large scale advertising campaign. Their hypothesis was that millennial women would enjoy the combination of the brand and coffee. The agency decided to investigate a number of facets around their idea and chose to use Facebook topic data to do so, because it was the most representative data source available.

Firstly, they wanted to see how their client’s brand was perceived by the target group in relation to its competitors. Secondly, they wanted to see how this group were engaging with hot drinks in the different countries the campaign was to run in. Finally, they wanted to know which media outlets and celebrities were influencing the target group so that they could make decisions on which channels to advertise in and which famous faces to approach to appear in the campaign.

They were able to do this with Facebook topic data by creating two indexes of stories. The first included posts and engagements about the brand, its competitors and hot drinks. The agency built classifiers to identify the brands and hot drinks mentioned across multiple languages. The second focused on interactions with links to ten top magazines targeted at young women, classifiers were used in this index to identify the influential magazines and the celebrities who appeared in the stories in those magazines. In just two weeks, the agency had gathered 3.5 million Facebook anonymized interactions from which to draw their insights.

So what did they find? First of all they found that engagement with the client brand was skewed away from the demographic group which they were intending to target. Only 31% of engagements with the brand came from females under 35, whereas comparable brands were getting 47-58% of their engagement from millennials. They found that the client’s brand was actually being engaged with much more by women over 55.

The agency were also able to see which types of hot drink were most popular in the different markets in which the campaign was planned to run. For example, they found that women in the USA were engaging most with stories about lattes, whereas in Germany the drink of choice was cappuccino and in the UK it was hot chocolate (tea was excluded from the analysis, I presume – as an Englishman – that this was because the brand knew it could not be improved upon).

From analysis of the magazine links shared by young women on Facebook the agency were not only able to find out which of the top women’s magazines were most popular online with their target demographic groups, but also which celebrities appearing in those magazines were driving the most engagement. While Jennifer Lawrence and Sharon Stone were the influencers most engaged with as a whole in the top women’s magazines during the period of analysis, women between 18 and 35 showed more interest in Taylor Swift and Kylie Jenner.

These insights meant that the agency could improve their campaign before it had even started. Firstly, they could expand the target demographic to older women to capitalise on the brand’s better relative engagement in that group. The agency were also able to build creative around different hot drinks in different markets, depending on the particular tastes of that country. In addition they were able to identify the best outlets for media placements in order to reach their target audience and identify which celebrity influencers might be worth working with in order to consolidate their position with older women, or conversely improve their profile with millennials.

Take a look at this post to see how these insights were gathered using PYLON.